Decision Trees with Hypotheses
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Abstract / Description
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Presents the concept of a hypothesis about the values of all attributes
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Provides tools for the experimental and theoretical study of decision trees with hypotheses
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Compares these decision trees with conventional decision trees that use only queries, each based on a single attribute
In this book, the concept of a hypothesis about the values of all attributes is added to the standard decision tree model, considered, in particular, in test theory and rough set theory. This extension allows us to use the analog of equivalence queries from exact learning and explore decision trees that are based on various combinations of attributes, hypotheses, and proper hypotheses (analog of proper equivalence queries). The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute.
Both experimental and theoretical results show that decision trees with hypotheses can have less complexity than conventional decision trees. These results open up some prospects for using decision trees with hypotheses as a means of knowledge representation and algorithms for computing Boolean functions. The obtained theoretical results and tools for studying decision trees with hypotheses are useful for researchers using decision trees and rules in data analysis. This book can also be used as the basis for graduate courses.
Keywords
Computational Intelligence, Decision Trees, Decision Tree Model, Test Theory, Rough Set Theory
Table of Contents
Front Matter
Decision Tables
- Front Matter
- Main Notions - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
- Dynamic Programming Algorithms for Minimization of Decision Tree Complexity - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
- Construction of Optimal Decision Trees and Deriving Decision Rules from Them - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
- Greedy Algorithms for Construction of Decision Trees with Hypotheses - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
- Decision Trees with Hypotheses for Recognition of Monotone Boolean Functions and for Sorting - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
Binary Information Systems and Infinite Families of Concepts
- Front Matter
- Infinite Binary Information Systems. Decision Trees of Types 1, 2, and 3 - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
- Infinite Binary Information Systems. Decision Trees of Types 4 and 5 - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
- Infinite Families of Concepts - Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
Back Matter
Publication Date
11-19-2022
Faculty / School
School of Mathematics and Computer Science (SMCS)
Department
Department of Computer Science
Was this content written or created while at IBA?
Yes
Series
Synthesis Lectures on Intelligent Technologies
e-ISBN/e-ISSN
978-3-031-08585-7
Rights Information
The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Recommended Citation
Azad, M., Chikalov, I., Hussain, S., Moshkov, M., & Zielosko, B. (2022). Decision Trees with Hypotheses. Retrieved from https://ir.iba.edu.pk/faculty-research-books/50
